Advancing photocatalytic water-splitting technology by reliable in silico design of the catalysts.

Hydrogen is a renewable, high-energy-density and non-polluting energy carrier, hence its production and use are deservedly in prime attention of policy makers worldwide. In that respect, producing hydrogen using solar energy and photocatalytic water splitting presents both viable and environmentally friendly technology. However, progressing this technology to a widely applicable level requires an abundant yet highly efficient photocatalyst. Although many semiconducting materials have been proposed and synthesized for this purpose, some of them possess a relatively large bandgap with poor absorption for solar flux, while others suffer from the low photoexcited carrier rate, both of which severely decrease the photocatalytic performance. In addition, excitonic effects are usually neglected in the photocatalyst design, which leads to incorrect predictions of important properties such as optical absorption and band edge positions, ultimately yielding incorrect estimates of the key parameter - the solar-to-hydrogen (STH) efficiency. This project aims to radically change this unfavorable picture, and develop reliable predictive methodology to identify materials for photocatalytic water splitting with highest STH efficiency. The success of this project will not only advance the current modeling of photocatalysts, but will also provide cost-saving shortcuts to targeted experimentation towards viable technology for the use of water and light for hydrogen production.

Keywords:PHOTOCATALYST, COMPUTATIONAL MODELING

Disciplines:Computational physics, Surface and interface chemistry, Catalysis, Photochemistry, Theoretical and computational chemistry not elsewhere classified